markdown:
# harming
Từ điển mở Wiktionary
## Tiếng Anh[
[sửa](/w/index.php?title=B%E1%BA%A3n_m%E1%BA%ABu:-eng-&action=edit§ion=T-1)]
### Động từ[
[sửa](/w/index.php?title=B%E1%BA%A3n_m%E1%BA%ABu:-verb-&action=edit§ion=T-1)] **harming**
#### Chia động từ[
[sửa](/w/index.php?title=B%E1%BA%A3n_m%E1%BA%ABu:-forms-&action=edit§ion=T-1)]
harm
|Dạng không chỉ ngôi|
|Động từ
|[harm](/wiki/harm) [Phân từ](/wiki/ph%C3%A2n_t%E1%BB%AB) [hiện tại](/wiki/hi%E1%BB%87n_t%E1%BA%A1i) **harming** [quá khứ](/wiki/qu%C3%A1_kh%E1%BB%A9) [harmed](/wiki/harmed) [Lối trình bày](/wiki/l%E1%BB%91i_tr%C3%ACnh_b%C3%A0y) [harm](/wiki/harm) [harm](/wiki/harm)hoặc [harmest](/w/index.php?title=harmest&action=edit&redlink=1)¹ [harms](/wiki/harms)hoặc [harmeth](/w/index.php?title=harmeth&action=edit&redlink=1)¹ [harm](/wiki/harm) [harm](/wiki/harm) [harm](/wiki/harm) [harmed](/wiki/harmed) [harmed](/wiki/harmed)hoặc [harmedst](/w/index.php?title=harmedst&action=edit&redlink=1)¹ [harmed](/wiki/harmed) [harmed](/wiki/harmed) [harmed](/wiki/harmed) [harmed](/wiki/harmed) [Tương lai](/wiki/t%C6%B0%C6%A1ng_lai) [will](/wiki/will)/ [shall](/wiki/shall)² [harm](/wiki/harm) [harm](/wiki/harm)hoặc [wilt](/wiki/wilt)/ [shalt](/wiki/shalt)¹ [harm](/wiki/harm) [harm](/wiki/harm) [harm](/wiki/harm) [harm](/wiki/harm) [harm](/wiki/harm) [Lối cầu khẩn](/wiki/l%E1%BB%91i_c%E1%BA%A7u_kh%E1%BA%A9n) [harm](/wiki/harm) [harm](/wiki/harm)hoặc [harmest](/w/index.php?title=harmest&action=edit&redlink=1)¹ [harm](/wiki/harm) [harm](/wiki/harm) [harm](/wiki/harm) [harm](/wiki/harm) [harmed](/wiki/harmed) [harmed](/wiki/harmed) [harmed](/wiki/harmed) [harmed](/wiki/harmed) [harmed](/wiki/harmed) [harmed](/wiki/harmed) [were](/wiki/were)to [harm](/wiki/harm)hoặc [should](/wiki/should) [harm](/wiki/harm) [harm](/wiki/harm)hoặc should [harm](/wiki/harm) [harm](/wiki/harm)hoặc should [harm](/wiki/harm) [harm](/wiki/harm)hoặc should [harm](/wiki/harm) [harm](/wiki/harm)hoặc should [harm](/wiki/harm) [harm](/wiki/harm)hoặc should [harm](/wiki/harm) [Lối mệnh lệnh](/wiki/l%E1%BB%91i_m%E1%BB%87nh_l%E1%BB%87nh) [harm](/wiki/harm) [harm](/wiki/harm) [harm](/wiki/harm)
- Cách chia động từ cổ.
- Thường nói
*will*; chỉ nói *shall*để nhấn mạnh. Ngày xưa, ở ngôi thứ nhất, thường nói *shall*và chỉ nói *will*để nhấn mạnh.

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markdown:
stylish
Từ điển mở Wiktionary
Bước tới:
dẫn lái
,
tìm
Mục lục
1
Tiếng Anh
1.1
Cách phát âm
1.2
Tính từ
1.3
Tham khảo
Tiếng Anh
[
sửa
]
Cách phát âm
[
sửa
]
IPA
:
/ˈstɑɪ.lɪʃ/
Tính từ
[
sửa
]
stylish
/ˈstɑɪ.lɪʃ/
Bảnh bao
,
diện
; hợp thời trang.
(
Thông tục
)
Kiểu cách
.
Tham khảo
[
sửa
]
Hồ Ngọc Đức,
Dự án Từ điển tiếng Việt miễn phí
(
chi tiết
)
Lấy từ “
http://vi.wiktionary.org/w/index.php?title=stylish&oldid=1376277
”
Thể loại
:
Mục từ tiếng Anh
Tính từ
Tính từ tiếng Anh
Trình đơn chuyển hướng
Công cụ cá nhân
Mở tài khoản
Đăng nhập
Không gian tên
Mục từ
Thảo luận
Biến thể
Các hiển thị
Tra
Sửa đổi
Xem lịch sử
Khác
Tìm kiếm
Chuyển hướng
Trang Chính
Cộng đồng
Thay đổi gần đây
Mục từ ngẫu nhiên
Trợ giúp
Thảo luận chung
Quyên góp
Chỉ mục
Ngôn ngữ
Chữ cái
Bộ thủ
Từ loại
Chuyên ngành
In/xuất ra
Tạo một quyển sách
Tải về dưới dạng PDF
Bản in được
Công cụ
Các liên kết đến đây
Thay đổi liên quan
Tải tập tin lên
Các trang đặc biệt
Liên kết thường trực
Thông tin trang
Trích dẫn trang này
Ngôn ngữ định nghĩa
Deutsch
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తెలుగు
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Trang này được sửa đổi lần cuối lúc 13:10 ngày 20 tháng 12 năm 2011.
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markdown:
# Xin game The Dark Knight Rises
Thảo luận trong '
[[Android] Hỏi đáp - CSKN](forums/android-hoi-dap-cskn.217/)' bắt đầu bởi [fcbkataly56](members/fcbkataly56.1423393/), [30/11/12](threads/xin-game-the-dark-knight-rises.1704191/).
Trả lời: 8, Xem: 1001.
Thảo luận trong '
[[Android] Hỏi đáp - CSKN](forums/android-hoi-dap-cskn.217/)' bắt đầu bởi [fcbkataly56](members/fcbkataly56.1423393/), [30/11/12](threads/xin-game-the-dark-knight-rises.1704191/).
Trả lời: 8, Xem: 1001.

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markdown:
[“The QMC algorithm forces us to write any simulation as an explicit function of uniform samples.” (p.8)] **A**s [posted a few days ago](https://xianblog.wordpress.com/2014/11/21/not-converging-to-london-for-an-extraordinary-read-paper/), Mathieu Gerber and Nicolas Chopin will read this afternoon a Paper to the Royal Statistical Society on their [sequential quasi-Monte Carlo sampling](https://xianblog.wordpress.com/2014/11/21/not-converging-to-london-for-an-extraordinary-read-paper/) paper. Here are some comments on the paper that are preliminaries to my written discussion (to be sent before the slightly awkward deadline of *Jan 2, 2015*).
Quasi-Monte Carlo methods are definitely
*not* popular within the (mainstream) statistical community, despite regular attempts by respected researchers like Art Owen and Pierre L’Écuyer to induce more use of those methods. It is thus to be hoped that the current attempt will be more successful, it being Read to the Royal Statistical Society being a major step towards a wide diffusion. I am looking forward to the collection of discussions that will result from the incoming afternoon (and bemoan once again having to miss it!).
“It is also the resampling step that makes the introduction of QMC into SMC sampling non-trivial.” (p.3)
At a mathematical level, the fact that randomised low discrepancy sequences produce both unbiased estimators
*and* error rates of order
means that randomised quasi-Monte Carlo methods should always be used, instead of regular Monte Carlo methods! So why is it not
*always* used?! The difficulty stands [I think] in expressing the Monte Carlo estimators in terms of a *deterministic* function of a *fixed* number of uniforms (and possibly of past simulated values). At least this is why I never attempted at crossing the Rubicon into the quasi-Monte Carlo realm… And maybe also why the step *had to* appear in connection with particle filters, which can be seen as dynamic importance sampling methods and hence enjoy a local iid-ness that relates better to quasi-Monte Carlo integrators than single-chain MCMC algorithms. For instance, each resampling step in a particle filter consists in a repeated multinomial generation, hence should have been turned into quasi-Monte Carlo ages ago. (However, rather than the basic solution drafted in Table 2, [lower variance solutions](http://robotics.stackexchange.com/a/484) like systematic and residual sampling have been proposed in the particle literature and I wonder if any of these is a special form of quasi-Monte Carlo.) In the present setting, the authors move further and apply quasi-Monte Carlo to the particles themselves. However, they still assume the deterministic transform
which the q-block on which I stumbled each time I contemplated quasi-Monte Carlo… So the fundamental difficulty with the whole proposal is that the generation from the Markov proposal
has to be of the above form. Is the strength of this assumption discussed anywhere in the paper? All baseline distributions there are normal. And in the case it does not easily apply, what would the gain bw in only using the second step (i.e., quasi-Monte Carlo-ing the multinomial simulation from the empirical cdf)? In a sequential setting with unknown parameters θ, the transform is modified each time θ is modified and I wonder at the impact on computing cost if the inverse cdf is not available analytically. And I presume simulating the θ’s cannot benefit from quasi-Monte Carlo improvements.
The paper obviously cannot get into every detail, obviously, but I would also welcome indications on the cost of deriving the Hilbert curve, in particular in connection with the dimension d as it has to separate all of the N particles, and on the stopping rule on m that means only H
m is used.
Another question stands with the multiplicity of low discrepancy sequences and their impact on the overall convergence. If Art Owen’s (1997) nested scrambling leads to the best rate, as implied by Theorem 7, why should we ever consider another choice?
In connection with Lemma 1 and the sequential quasi-Monte Carlo approximation of the evidence, I wonder at any possible Rao-Blackwellisation using all proposed moves rather than only those accepted. I mean, from a quasi-Monte Carlo viewpoint, is Rao-Blackwellisation easier and is it of any significant interest?
What are the computing costs and gains for forward and backward sampling? They are not discussed there. I also fail to understand the trick at the end of 4.2.1, using SQMC on a single vector instead of (t+1) of them. Again assuming inverse cdfs are available? Any connection with the Polson et al.’s
[particle learning](http://projecteuclid.org/euclid.ss/1280841735) literature?
Last questions: what is the (learning) effort for lazy me to move to SQMC? Any hope of stepping outside particle filtering?

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